Deep learning for precision medicine: Guiding laser therapy in ischemic retinal diseases
Authors: Ting Fang Tan, Shelley Yin-His Chang, Daniel Shu Wei Ting
Journal: Cell Reports Medicine (2023)

In this issue of Cell Reports Medicine, Zhao and colleagues1Zhao X. Lin Z. Yu S. Xiao J. Xie L. Xu Y. Tsui C.K. Cui K. Zhao L. Zhang G. et al.An artificial intelligence system for the whole process from diagnosis to treatment suggestion of ischemic retinal diseases.Cell Rep. Med. 2023; 4101197https://doi.org/10.1016/j.xcrm.2023.101197Abstract Full Text Full Text PDF Scopus (0) Google Scholar report a multi-tasking artificial intelligence system that can assist the whole process of fundus fluorescein angiography (FFA) imaging and reduce the reliance on retinal specialists in FFA examination. In this issue of Cell Reports Medicine, Zhao and colleagues1Zhao X. Lin Z. Yu S. Xiao J. Xie L. Xu Y. Tsui C.K. Cui K. Zhao L. Zhang G. et al.An artificial intelligence system for the whole process from diagnosis to treatment suggestion of ischemic retinal diseases.Cell Rep. Med. 2023; 4101197https://doi.org/10.1016/j.xcrm.2023.101197Abstract Full Text Full Text PDF Scopus (0) Google Scholar report a multi-tasking artificial intelligence system that can assist the whole process of fundus fluorescein angiography (FFA) imaging and reduce the reliance on retinal specialists in FFA examination. Artificial intelligence (AI)-driven precision medicine has revolutionized the field of medicine. Along the continuum from screening, diagnosis, treatment, to monitoring, care can be tailored to the individual. Deep-learning systems (DLSs) have been extensively applied to ophthalmology over the past decade, a field where clinical practice is predominantly guided by multimodal imaging.2Ting D.S.W. Cheung C.Y.-L. Lim G. Tan G.S.W. Quang N.D. Gan A. Hamzah H. Garcia-Franco R. San Yeo I.Y. Lee S.Y. et al.Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes.JAMA. 2017; 318: 2211-2223https://doi.org/10.1001/jama.2017.1815Crossref PubMed Scopus (0) Google Scholar However, beyond disease diagnosis and classification, increasing work has focused on personalizing patient care.3De Fauw J. Ledsam J.R. Romera-Paredes B. Nikolov S. Tomasev N. Blackwell S. Askham H. Glorot X. O'Donoghue B. Visentin D. et al.Clinically applicable deep learning for diagnosis and referral in retinal disease.Nat. Med. 2018; 24: 1342-1350Crossref PubMed Scopus (1328) Google Scholar In diabetic retinopathy (DR), predicting progression based on clinical and demographical variables and color fundus photographs (CFP) has been demonstrated at 2–5 years from baseline.4Bora A. Balasubramanian S. Babenko B. Virmani S. Venugopalan S. Mitani A. de Oliveira Marinho G. Cuadros J. Ruamviboonsuk P. Corrado G.S. et al.Predicting the risk of developing diabetic retinopathy using deep learning.Lancet. Digit. Health. 2021; 3: e10-e19https://doi.org/10.1016/S2589-7500(20)30250-8Abstract Full Text Full Text PDF PubMed Scopus (92) Google Scholar This potentially translates to personalizing DR screening intervals beyond the current 1- to 2-yearly fixed intervals to maximize resource capacity in the face of the increasing disease prevalence. Furthermore, large language models enable unsupervised self-learning on vast amounts of unlabeled data in medicine.5Thirunavukarasu A.J. Ting D.S.J. Elangovan K. Gutierrez L. Tan T.F. Ting D.S.W. Large language models in medicine.Nat. Med. 2023; 29: 1930-1940Crossref PubMed Scopus (4) Google Scholar,6Tan T.F. Thirunavukarasu A.J. Campbell J.P. Keane P.A. Pasquale L.R. Abramoff M.D. Kalpathy-Cramer J. Lum F. Kim J.E. Baxter S.L. Ting D.S.W. Generative Artificial Intelligence through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges.Ophthalmol. Sci. 2023; 100394https://doi.org/10.1016/j.xops.2023.100394Abstract Full Text Full Text PDF Google Scholar The recent release of RETFound, a foundational model from millions of unlabeled retinal images, achieved accurate diagnosis and prognostication of ocular, as well as systemic, diseases.7Zhou Y. Chia M.A. Wagner S.K. Ayhan M.S. Williamson D.J. Struyven R.R. Liu T. Xu M. Lozano M.G. Woodward-Court P. et al.A foundation model for generalizable disease detection from retinal images.Nature. 2023; https://doi.org/10.1038/s41586-023-06555-xCrossref Scopus (0) Google Scholar Retinal ischemia is important in retinal vascular diseases to prognosticate disease trajectory and visual outcome in each patient. Ischemic eyes are at an increased risk of complications, including neovascularization, neovascular glaucoma, vitreous hemorrhage, and cystoid macular edema.8Scott I.U. Campochiaro P.A. Newman N.J. Biousse V. Retinal vascular occlusions.Lancet. 2020; 396: 1927-1940https://doi.org/10.1016/S0140-6736(20)31559-2Abstract Full Text Full Text PDF PubMed Scopus (65) Google Scholar Therefore, the identification and quantification of ischemia is fundamental in guiding principles of management and frequency of follow-up of retinal vascular diseases. In these ischemic retinal vein occlusion (RVO) eyes, careful observation with frequent follow-up monitoring was recommended to perform prompt laser photocoagulation when neovascularization develops. For DR, peripheral retinal ischemia was found to be strongly associated with proliferative DR.9Ehlers J.P. Jiang A.C. Boss J.D. Hu M. Figueiredo N. Babiuch A. Talcott K. Sharma S. Hach J. Le T. et al.Quantitative Ultra-Widefield Angiography and Diabetic Retinopathy Severity: An Assessment of Panretinal Leakage Index, Ischemic Index and Microaneurysm Count.Ophthalmology. 2019; 126: 1527-1532https://doi.org/10.1016/j.ophtha.2019.05.034Abstract Full Text Full Text PDF PubMed Scopus (61) Google Scholar To ascertain the extent of retinal ischemia, fundus fluorescein angiography (FFA) remains the gold standard in assessing the extent of ischemia. In eyes with RVO, the Central Vein Occlusion Study (CVOS) defined eyes with central RVO as ischemic when 10 or greater disc diameters of retinal non-perfusion were noted on 7-field FFA; while the Branch Vein Occlusion Study (BVOS) defined eyes with branch RVO as ischemic when 5 or greater disc diameters of retinal non-perfusion were noted.7Zhou Y. Chia M.A. Wagner S.K. Ayhan M.S. Williamson D.J. Struyven R.R. Liu T. Xu M. Lozano M.G. Woodward-Court P. et al.A foundation model for generalizable disease detection from retinal images.Nature. 2023; https://doi.org/10.1038/s41586-023-06555-xCrossref Scopus (0) Google Scholar Although optical coherence tomography angiography (OCTA), including the widefield OCT-A, has emerged as an effective alternative to FFA, these newer imaging techniques could still be limited by motion artifacts and difficulty in distinguishing non-specific signal voids from non-perfusion areas (NPAs). The advantages of FFA, as compared to OCT-A, include a wider field of view of the retina peripheries using widefield imaging and evaluation of leakage and vascular filling time. The key challenge of FFA, however, lies in its need for manual interpretation by human experts, which is time consuming, subjective, and variable. Furthermore, existing work on automating FFA evaluation using DLS has been limited to single retinal disease or solely for diagnosis. To tackle this clinical challenge, Zhao et al. developed a DLS (Ai-Doctor) to automate the clinical workflow from diagnosis to treatment planning of DR and branch RVO based on FFA.1Zhao X. Lin Z. Yu S. Xiao J. Xie L. Xu Y. Tsui C.K. Cui K. Zhao L. Zhang G. et al.An artificial intelligence system for the whole process from diagnosis to treatment suggestion of ischemic retinal diseases.Cell Rep. Med. 2023; 4101197https://doi.org/10.1016/j.xcrm.2023.101197Abstract Full Text Full Text PDF Scopus (0) Google Scholar This multi-step DLS included phase identification and disease diagnosis on FFA and segmentation of NPAs to derive their proposed clinically applicable ischemia index (CAII), intended to suggest the need for laser therapy. Ai-Doctor achieved AUC of >0.90 in FFA phase identification and disease diagnosis. Similar performance was further maintained in external datasets. For NPA segmentation, >85% dice similarity coefficient (DSC) was demonstrated in internal and external datasets. Notably, Ai-Doctor consistently segmented NPAs in separate datasets of unencountered retinal vascular diseases (Central RVO, retinal vasculitis) with >80% DSC. Finally, optimal thresholds of CAII values (based on the proportion of NPA over total visualized retinal area on 55° FFA) were determined to guide likelihood for laser therapy. The Ai-Doctor illustrated the ability to overcome the clinical challenge of FFA assessment across several retinal vascular diseases, with its large labeled training dataset and generalizability of NPA segmentation to other retinal vascular diseases unencountered in model training. Furthermore, the multi-step workflow expands its capability beyond disease diagnosis to influence treatment decision with its proposed CAII as a quantitative threshold to guide the potential need for laser therapy and monitor progression during follow-ups. To translate Ai-Doctor into the real-world clinical practice, several areas could be considered for future work. First, this study was tested primarily on the Chinese population and a specific widefield FFA imaging device, and its generalizability to other populations and FFA imaging devices still remains uncertain. Second, it would be interesting to evaluate the health economic analyses to assess the clinical impact in real-world settings.10Yuan A. Lee A.Y. Artificial intelligence deployment in diabetic retinopathy: the last step of the translation continuum.Lancet. Digit. Health. 2022; 4: e208-e209https://doi.org/10.1016/S2589-7500(22)00027-9Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar Third, future studies could also consider using the more economical, accessible, and non-invasive fundus imaging to evaluate the extent of retinal ischemia, comparing them with the FFA results. Should these prove to be clinically effective, the AI technology potentially could have a wider reach in real-world settings. Prevention is better than cure. Given the increasing prevalence of diabetes globally, more populations potentially may suffer from ischemic retinal diseases. It is prudent, therefore, to harness the power of cutting-edge AI techniques, for example, deep learning and large language models, to focus on preventive and precision medicine. The use of generative AI and large language models has certainly created a tremendous interest worldwide. Given the surge in performance, this would be another potential powerful AI technology to be considered to improve patients' clinical outcome and experience. Lastly, it is also important to consider the health economic, trustworthy, and explainable AI to improve the clinical translation and adoption of these technologies in real-world settings. In summary, the management of ischemic retinal diseases requires both systemic and ocular management. The use of telehealth and state-of-art digital and AI technologies could help personalize patients' care, streamlining clinical workflows and optimizing resource capacity to improve patients' clinical outcome and experience. D.S.W.T. is supported by grants from the National Medical Research Council Singapore (NMRC/HSRG/0087/2018; MOH-000655-00; MOH-001014-00), Duke-NUS Medical School Singapore (Duke-NUS/RSF/2021/0018; 05/FY2020/EX/15-A58), and Agency for Science, Technology and Research Singapore (A20H4g2141; H20C6a0032) for research in artificial intelligence. D.S.W.T. holds a patent on a deep-learning system for the detection of retinal diseases. An artificial intelligence system for the whole process from diagnosis to treatment suggestion of ischemic retinal diseasesZhao et al.Cell Reports MedicineSeptember 20, 2023In BriefZhao et al. develop a multi-tasking artificial intelligence system (Ai-Doctor) capable of automated image phases identification, ischemic retinal diseases diagnosis, and ischemic areas segmentation using fundus fluorescein angiography images and propose a clinically applicable ischemia index, potentially improving the whole management process of ischemic retinal diseases. Full-Text PDF Open Access